Hybrid Search (Text + Vector) w/ Re-Ranking Performance

The Problem Current hybrid search methods that combine vector and keyword results often suffer from high latency. The system typically waits to retrieve and rank all potential matches from both sources before calculating a final score. This "stop-and-wait" approach creates a performance bottleneck, especially when users only require the top few results.

The Solution: Streaming Windows This feature introduces a streaming window architecture to merge search results in real-time.

Concurrent Processing: The engine processes vector and keyword result streams simultaneously.

Windowed Ranking: A sliding window identifies high-ranking matches as they appear. If a result’s score in one stream is high enough to guarantee its final position, the system can promote it immediately.

Early Termination: The search process stops once the requested number of results (e.g., LIMIT 10) is finalized, rather than scanning the entire dataset.

The Benefits

Reduced Latency: Delivers the most relevant results faster by eliminating the need for full dataset scans.

Balanced Accuracy: Merges semantic intent with keyword precision without the usual performance trade-offs.

Resource Efficiency: Minimizes CPU and memory usage by processing only the data necessary to satisfy the query limits.